Códigos con estructura temporal en neurociencia computacional / Time-structured codes in computational neurocience

Gonzalo Cogno, Soledad (2017) Códigos con estructura temporal en neurociencia computacional / Time-structured codes in computational neurocience. PhD in Physics, Universidad Nacional de Cuyo, Instituto Balseiro.

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Abstract in Spanish

La visión más tradicional del código neuronal se basa en suponer que las neuronas representan información variando su tasa de disparo. Existen estudios más recientes, sin embargo, que demuestran que en el sistema nervioso la información se codifica por varios mecanismos actuando en paralelo, siendo la frecuencia tan sólo uno de los códigos en juego. Existen otros códigos basados en la modulación de la localización temporal de los disparos, ya sea estructurando el tren de spikes de neuronas individuales en escalas de unos pocos milisegundos, coordinando los disparos de pares de neuronas, o sincronizándolos respecto de las fluctuaciones del potencial eléctrico circundante. En esta tesis exploramos cuatro ejemplos de tales códigos. Utilizando herramientas de la teoría de sistemas dinámicos y la teoría de la información, demostramos que tanto en modelos teóricos como en simulaciones numéricas los códigos temporalmente estructurados se manifiestan a nivel de neuronas individuales y en poblaciones, con consecuencias en la codificación, y en procesos de aprendizaje. Posteriormente analizamos datos electrofisiológicos de trenes de spikes y potenciales de campo registrados en el lóbulo temporal de roedores que realizan tareas de exploración y locomoción. Los registros muestran códigos temporalmente estructurados, donde las neuronas individuales generan secuencias de spikes que codifican información del potencial local de campo. Los potenciales de campo, a su vez, oscilan a frecuencia theta, observándose saltos abruptos en la fase de la oscilación asociados a eventos comportamentales relevantes. El análisis sugiere que la estructura temporal de las señales electrofisiológicas tiene información del estado de movimiento del roedor, y en particular, permite identificar eventos en los que el animal parece identificar con precisión su ubicación en el espacio, y corregir el error acumulado hasta el momento. Concluimos que los códigos temporalmente estructurados son ubicuos, y tienen relevancia funcional en el sistema nervioso de los mamíferos.

Abstract in English

The traditional view of the neural code assumes that neurons represent information in their mean firing rates, measured in windows of tens or hundreds of milliseconds. More recent studies, however, demonstrate that in the nervous system, information is encoded through several mechanisms acting in parallel, the mean firing frequency being only one of several codes in play. Other codes modulate the precise timing of spikes, either structuring the action potentials of single cells in scales of one or a few milliseconds, coordinating the firing of pairs of neurons, or synchronizing them with respect of the fluctuations of the electric potential of the local extracellular environment. In this thesis we explore four examples of such codes. Using tools of the theory of dynamical systems and information theory, we demonstrate that both in theoretical models and in numerical simulations, temporally structured neural codes appear both at the level of single cells and whole populations, affecting both the actual encoding of stimuli, and learning processes.We also analyze electrophysiological recordings of single spikes and field potentials measured in the temporal lobe of awake rodents navigating and exploring the environment. Neurons produce spike trains that can be parsed into sequences of stereotyped patterns encoding information about the surrounding local field potentials. Such potentials, in turn, exhibit prominent theta oscillations, and interestingly, they contain sudden phase resettings for specific behavioral events. The analysis suggests that the fine-temporal structure of the electrophysiological signals encodes the state of motion of the animal, and in particular, allows us to detect episodes where the animal seems to identify its location precisely, and correct the error accumulated thus far.We conclude that temporally structured codes are ubiquitous, and have functional relevance in the mammal nervous system.

Item Type:Thesis (PhD in Physics)
Keywords:Plasticity; Plasticidad;[Neurocience; Neurociencia; Neuronal code; Código neuronal; Local field potential; Potencial de campo local; Single cell models; Modelo de neurona única; Neural network models; Modelo de redes neuronales]
References:Abbott, L.F., Nelson, S.B. (2000). Synaptic plasticity: taming the beast. Nature Neuroscience, 3, 1178-1183. Adrian, E. D. (1928). The basis of sensation. New York, NY, US: W W Norton & Co Barlow, H. B. (1953). Action potentials from the frog’s retina. The Journal of Physiology, 119(1), 58. Barlow, H. B. (1953). Summation and inhibition in the frog’s retina. The Journal of Physiology, 119(1), 69. Barlow, J. S. (1993). The electroencephalogram: its patterns and origins. MIT press. Batschelet, E. (1981). Circular statistics in biology. Academic Press, 111 Fifth Ave., New York, NY 10003, 1981, 388. Ben-Yishai, R., Bar-Or, R.L., Sompolinsky, H. (1995). Theory of orientation tuning in visual cortex. Proc Natl Acad Sci USA 92, 3844-3848. Bi G, PooM(1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience 18, 10464-10472. Bonhoeffer T, Grinvald A (1991) Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns. Nature, 353(6343), 429-431. Borkowski LS (2010) Multimodal transition and stochastic antiresonance in squid giant axons. Physical Review E 82: 041909. Borkowski LS (2011) Bistability and resonance in the periodically stimulated Hodgkin-Huxley model with noise. Physical Review E 83: 051901. Bryant, H. L., Segundo, J. P. (1976). Spike initiation by transmembrane current: a white-noise analysis. The Journal of Physiology, 260(2), 279. Buzsaki, G. (2006). Rhythms of the Brain. Oxford University Press. Buzsáki, G., Moser, E. I. (2013). Memory, navigation and theta rhythm in the hippocampalentorhinal system. Nature Neuroscience, 16(2), 130-138. Buzsáki, G., Anastassiou, C. A., Koch, C. (2012). The origin of extracellular fields and currents-EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience, 13(6), 407-420. Canto, C. B., Wouterlood, F. G., Witter, M. P. (2008). What does the anatomical organization of the entorhinal cortex tell us?. Neural plasticity, 2008. Cecchi, G. A., Sigman, M., Alonso, J. M., Martínez, L., Chialvo, D. R., Magnasco, M. O. (2000). Noise in neurons is message dependent. Proceedings of the National Academy of Sciences, 97(10), 5557-5561. Constantinou, M., Elijah, D. H., Squirrell, D., Gigg, J., Montemurro, M. A. (2015). Phase-locking of bursting neuronal firing to dominant LFP frequency components. Biosystems, 136, 73-79. Coombes, S., Bressloff, P. C. (1999). Mode locking and Arnold tongues in integrate-and-fire neural oscillators. Physical Review E, 60(2), 2086. Coombes, S., Owen, M. R., Smith, G. D. (2001). Mode locking in a periodically forced integrateand-fire-or-burst neuron model. Physical Review E, 64(4), 041914. Corey, J., Scholl, B. (2012). Cortical Selectivity through Random Connectivity. J Neurosci 32(30),10103-10104. Cossell, L., Iacaruso, M.F., Muir, D.R., Houlton, R., Sader, E.N., Ko, H., Hofer, S.B., Mrsic-Flogel, T.D. (2015). Functional organization of excitatory synaptic strength in primary visual cortex. Nature 518, 399-403. Couey, J. J., Witoelar, A., Zhang, S. J., Zheng, K., Ye, J., Dunn, B., Czajkowski R., Moser M. B., Moser E. I., Witter, M. P. (2013). Recurrent inhibitory circuitry as a mechanism for grid formation. Nature Neuroscience, 16(3), 318-324. Cover, T. M., Thomas, J. A. (2012). Elements of information theory. John Wiley & Sons. Destexhe, A., Rudolph, M., Pare, D. (2003). The high-conductance state of neocortical neurons in vivo. Nature Reviews Neuroscience 4:739-751. Dräger, U. C. (1975). Receptive fields of single cells and topography in mouse visual cortex. Journal of Comparative Neurology, 160(3), 269-289. Einevoll, G. T., Pettersen, K. H., Devor, A., Ulbert, I., Halgren, E., Dale, A. M. (2007). Laminar population analysis: estimating firing rates and evoked synaptic activity from multielectrode recordings in rat barrel cortex. Journal of Neurophysiology, 97(3), 2174-2190. Eyherabide, H. G., Rokem, A., Herz, A. V., Samengo, I. (2008). Burst firing is a neural code in an insect auditory system. Frontiers in Computational Neuroscience. 2,3. doi:10.3389/neuro.10.003.2008. Eyherabide, H. G., Rokem, A., Herz, A. V., Samengo, I. (2009). Bursts generate a non-reducible spike pattern code. Frontiers in Neuroscience. 3, 8–14. doi:10.3389/neuro.01.002.2009 Eyherabide, H. G., Samengo, I. (2010). The information transmitted by spike patterns in single neurons. Journal of Physiology Paris, 104(3), 147-155. Eyherabide, H. G., Samengo, I. (2010). Time and category information in pattern-based codes. Frontiers in Computational Neuroscience. 4, 145. doi:10.3389/fncom.2010.00145 Fellous, J. M., Houweling, A. R., Modi, R. H., Rao, R. P. N., Tiesinga, P. H. E., Sejnowski, T. J. (2001). Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons. Journal of Neurophysiology, 85(4), 1782-1787. Feldman, D.E. (2000). Timing-based LTP and LTD at vertical inputs to layer II/III pyramidal cells in rat barrel cortex. Neuron 27, 45-56. Fyhn, M., Molden, S., Witter, M. P., Moser, E. I., Moser, M. B. (2004). Spatial representation in the entorhinal cortex. Science, 305(5688), 1258-1264. Fyhn, M., Hafting, T., Witter, M. P., Moser, E. I., Moser, M. B. (2008). Grid cells in mice. Hippocampus, 18(12), 1230-1238. Gigg, J., Finch, D. M., O’Mara, S. M. (2000). Responses of rat subicular neurons to convergent stimulation of lateral entorhinal cortex and CA1 in vivo. Brain research, 884(1), 35-50. Golomb, D., Hansel, D., Shraiman, B., Sompolinsky, H. (1992). Clustering in globally coupled phase oscillators. Physical Review A, 45(6), 3516. Haas, J.S., Nowotny, T., Abarbanel, H.D.I. (1996). Spike-Timing-Dependent Plasticity of Inhibitory Synapses in the Entorhinal Cortex. Journal of Neurophysiology, 96, 3305-3313. Golomb, D., Rinzel, J. (1993). Dynamics of globally coupled inhibitory neurons with heterogeneity. Physical Review E, 48(6), 4810. Hafting, T., Fyhn, M., Molden, S., Moser, M. B., Moser, E. I. (2005). Microstructure of a spatial map in the entorhinal cortex. Nature, 436(7052), 801-806. Haider, B., Duque, A., Hasenstaub, A. R., McCormick, D. A. (2006). Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. The Journal of Neuroscience, 26(17), 4535-4545. Hansel, D., Mato, G. (2013). Short-term plasticity explains irregular persistent activity in working memory tasks. Journal of Neuroscience, 33(1), 133-149. Hansel, D., van Vreeswijk, C. (2002). How noise contributes to contrast invariance of orientation tuning in cat visual cortex. Journal of Neuroscience, 22(12), 5118-5128. Hansel, D., van Vreeswijk, C. (2012). The mechanism of orientation selectivity in primary visual cortex without a functional map. Journal of Neuroscience, 32(12), 4049-4064. Hardcastle, K., Ganguli, S., Giocomo, L. M. (2015). Environmental boundaries as an error correction mechanism for grid cells. Neuron, 86(3), 827-839. Harris, K. D., Hirase, H., Leinekugel, X., Henze, D. A., Buzsáki, G. (2001). Temporal interaction between single spikes and complex spike bursts in hippocampal pyramidal cells. Neuron, 32(1), 141-149. Hartline, H. K. (1974). Studies on Excitation and Inhibition in the Retina. F. Ratliff (Ed.). London: Chapman Hall. Hodgkin, A. L., Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117(4), 500. Hofer SB, Ko H, Pichler B, Vogelstein J, Ros H, Zeng H, Lein E, Lesica NA, Mrsic-Flogel TD (2011) Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex. Nature Neuroscience 14, 1045-1052. Holmgren C, Harkany T, Svennenfors B, Zilberter Y (2003) Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. J Physiol 551 139-153. Holt GR, Softky WR, Koch C, Douglas RJ (1996) Comparison of discharge variability in vitro and in vivo in cat visual cortex neurons. Journal Neurophysiology 75, 1806-1814. Honeycutt, R. L. (1992). Stochastic runge-kutta algorithms. I. White noise. Physical Review A, 45(2), 600. Hubel DH, Wiesel TN (1961) Integrative action in the cat’s lateral geniculate body. J Physiol (Lond) 155, 385-398. Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol (Lond) 160, 106-154. Hunter, J. D., Milton, J. G., Thomas, P. J., Cowan, J. D. (1998). Resonance effect for neural spike time reliability. Journal of Neurophysiology, 80(3), 1427-1438. Izhikevich, E. M. (2007). Dynamical systems in neuroscience. MIT press. Jacobs, J.,Weidemann, C. T., Miller, J. F., Solway, A., Burke, J. F.,Wei, X. X., Suthana N., Sperling M. R., Sharan A. D., Fried I., Kahana, M. J. (2013). Direct recordings of grid-like neuronal activity in human spatial navigation. Nature Neuroscience, 16(9), 1188-1190. Jia, H., Rochefort, N. L., Chen, X., Konnerth, A. (2011) In vivo two-photon imaging of sensoryevoked dendritic calcium signals in cortical neurons. Nature Protocols 6, 28-35. Kamondi, A., Acsády, L., Wang, X. J., Buzsáki, G. (1998). Theta oscillations in somata and dendrites of hippocampal pyramidal cells in vivo: Activity-dependent phase-precession of action potentials. Hippocampus, 8(3), 244-261. Kandel, E., Schwartz, J., Jessell, T. (2000). Principles of Neural Science. Kayser, C., Montemurro, M. A., Logothetis, N. K., Panzeri, S. (2009). Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron, 61(4), 597-608. Kepecs, A.,Wang, X. J. (2000). Analysis of complex bursting in cortical pyramidal neuron models. Neurocomputing, 32, 181-187. Kepecs, A.,Wang, X. J., Lisman, J. (2002). Bursting neurons signal input slope. Journal of Neuroscience, 22(20), 9053-9062. Kepecs, A., Lisman, J. (2003). Information encoding and computation with spikes and bursts. Network: Computation in neural systems, 14(1), 103-118. Killian, N. J., Jutras, M. J., Buffalo, E. A. (2012). A map of visual space in the primate entorhinal cortex. Nature, 491(7426), 761-764. Ko, H., Hofer, S.B., Buchanan, K.A., Sjöström, P.J., Mrsic-Flogel, T.D. (2011) Functional specificity of local synaptic connections in necortical networks. Nature 473, 87-91. Ko, H., Cossell, L., Baragli, C., Antolik, J., Clopath, C., Hofer, S., Mrsic-Flogel, T. (2013) The emergence of functional microcircuits in visual cortex. Nature 496, 96-100. Koch, C. (1999) Biophysics of Computation, Oxford, Oxford University Press. Kropff, E., Carmichael, J. E., Moser, M. B., Moser, E. I. (2015). Speed cells in the medial entorhinal cortex. Nature. Kuffler, S.W. (1953). Discharge patterns and functional organization of mammalian retina. Journal of Neurophysiology, 16(1), 37-68. Laing, C. R., Longtin, A. (2003). Periodic forcing of a model sensory neuron. Physical Review E, 67(5), 051928. Latuske, P., Toader, O., Allen, K. (2015). Interspike Intervals Reveal Functionally Distinct Cell Populations in the Medial Entorhinal Cortex. Journal of Neuroscience, 35(31), 10963-10976. Lisman, J. E., Idiart, M. A. (1995). Storage of 7 plus/minus 2 short-term memories in oscillatory subcycles. Science, 267(5203), 1512. Lisman, J. (2005). The theta/gamma discrete phase code occuring during the hippocampal phase precession may be a more general brain coding scheme. Hippocampus, 15(7), 913-922. Logothetis, N. K. (2003). The underpinnings of the BOLD functional magnetic resonance imaging signal. Journal of Neuroscience, 23(10), 3963-3971. Mainen, Z. F., Sejnowski, T. J. (1995). Reliability of spike timing in neocortical neurons. Science, 268(5216), 1503. Mardia, K. V., Jupp, P. E. (2009). Directional statistics (Vol. 494). John Wiley & Sons. Markram, H., Lübke, J., Frotscher, M., Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275(5297), 213-215. McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I., Moser, M. B. (2006). Path integration and the neural basis of the’cognitive map’. Nature Reviews Neuroscience, 7(8), 663-678. Mizuseki, K., Sirota, A., Pastalkova, E., Buzsáki, G. (2009). Theta oscillations provide temporal windows for local circuit computation in the entorhinal-hippocampal loop. Neuron, 64(2), 267- 280. Molle, M., Born, J. (2011). Slow oscillations orchestrating fast oscillations and memory consolidation. Slow Brain Oscillations of Sleep, Resting State and Vigilance: Proceedings of the 26th International Summer School of Brain Research, Held at the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands, 29 June-2 July, 2010, 193, 93. Montemurro, M. A., Senatore, R., Panzeri, S. (2007). A downward biased estimator of spike timing information. Neurocomputing, 70(10), 1777-1781. Montemurro, M. A., Senatore, R., Panzeri, S. (2007). Tight data-robust bounds to mutual information combining shuffling and model selection techniques. Neural Computation, 19(11), 2913-2957. Montemurro, M. A., Rasch, M. J., Murayama, Y., Logothetis, N. K., Panzeri, S. (2008). Phase-offiring coding of natural visual stimuli in primary visual cortex. Current Biology, 18(5), 375-380. Moser, E. I., Roudi, Y., Witter, M. P., Kentros, C., Bonhoeffer, T., Moser, M. B. (2014). Grid cells and cortical representation. Nature Reviews Neuroscience, 15(7), 466-481. O’Keefe, J., Dostrovsky, J. (1971). The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain research, 34(1), 171-175. O’Keefe, J., Recce, M. L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus, 3(3), 317-330. Ohki, K., Chung, S., Ch’ng, Y. H., Kara, P., Reid, R. C. (2005). Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Nature, 433(7026), 597-603. Ohki, K., Reid, R.C. (2007). Specificity and randomness in the visual cortex. Current Opinion in Neurobiology, 17(4), 401-407. Panzeri, S., Senatore, R., Montemurro, M. A., Petersen, R. S. (2007). Correcting for the sampling bias problem in spike train information measures. Journal of Neurophysiology, 98(3), 1064-1072. Pettersen, K. H., Hagen, E., Einevoll, G. T. (2008). Estimation of population firing rates and current source densities from laminar electrode recordings. Journal of Computational Neuroscience, 24(3), 291-313. Pinsky, P. F., Rinzel, J. (1994). Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. Journal of Computational Neuroscience, 1(1-2), 39-60. Press, W., Vetterling, W., Teukolsky, S., Flannery, B. (1992). Numerical recipes in FORTRAN 77: the art of scientific computing. Cambridge, UK: Cambridge University Press. Ranck, J. B. (1973). Studies on single neurons in dorsal hippocampal formation and septum in unrestrained rats: Part I. Behavioral correlates and firing repertoires. Experimental neurology, 41(2), 462-531. Rasch, B., Born, J. (2013). About sleep’s role in memory. Physiological Reviews, 93(2), 681-766. Rauch, A., La Camera, G., Lüscher, H. R., Senn, W., Fusi, S. (2003). Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo–like input currents. Journal of Neurophysiology, 90(3), 1598-1612. Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., Harris, K.D. (2010). The Asynchronous State in Cortical Circuits. Science 327, 587-590. Rieke, F. (1999). Spikes: exploring the neural code. MIT press. Ringach, D. L., Shapley, R. M., Hawken, M. J. (2002). Orientation selectivity in macaque V1: diversity and laminar dependence. Journal of Neuroscience, 22(13), 5639-5651. Rosenbaum, R., Doiron, B. (2014). Balanced Networks of Spiking Neurons with Spatially Dependent Recurrent Connections. Physical Review X, 4(2), 021039. Rowat, P. F., Greenwood, P. E. (2011). Identification and continuity of the distributions of burstlength and interspike intervals in the stochastic Morris-Lecar neuron. Neural computation, 23(12), 3094-3124. Rubin, J., Lee, D.D., Sompolinsky, H. (2001). Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity. Physical Review Letters 86, 364-367. Samengo, I., Montemurro, M. A. (2010). Conversion of phase information into a spike-count code by bursting neurons. PloS One, 5(3), e9669. Samengo, I., Mato, G., Elijah, D. H., Schreiber, S., Montemurro, M. A. (2013). Linking dynamical and functional properties of intrinsically bursting neurons. Journal of Computational Neuroscience, 35(2), 213-230. Sargolini, F., Fyhn, M., Hafting, T., McNaughton, B. L., Witter, M. P., Moser, M. B., Moser, E. I. (2006). Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science, 312(5774), 758-762. Scholl, B., Tan, A.Y.Y., Corey, J., Priebe, N.J. (2013). Emergence of Orientation Selectivity in the Mammalian Visual Pathway. Journal of Neuroscience 33, 10616-10634. Schreiber, S., Fellous, J.M., Whitmer, D., Tiesinga, P.H.E., Sejnowski, T.J. (2003). A new correlation-based measure of spike timing reliability. Neurocomputing 52-4:925–931. Schreiber, S., Fellous, J.M., Tiesinga, P.H.E., Sejnowski, T.J. (2004). Influence of ionic conductances on spike timing reliability of cortical neurons for suprathreshold rhythmic inputs. Journal of Neurophysiology 91:194–205. Schreiber S., Samengo I., Herz A.V.M. (2009). Two distinct mechanisms shape the reliability of neural responses. Journal of Neurophysiology 101: 2239-2251 Shannon, C. E. (1948). A mathematical theory of communication, Bell System technical Journal 27: 379-423 and 623–656. Mathematical Reviews (MathSciNet): MR10, 133e. Sharp, P. E., Green, C. (1994). Spatial correlates of firing patterns of single cells in the subiculum of the freely moving rat. Journal of Neuroscience, 14(4), 2339-2356. Shu, Y., Hasenstaub, A., McCormick, D.A. (2003). Turning on and off recurrent balanced cortical activity. Nature 423:288-293. Skaggs,W. E., McNaughton, B. L., Gothard, K. M., Markus, E. J. (1993). An information-theoretic approach to deciphering the hippocampal code. In Advancesin Neural Information Processing Systems, eds. S. J. Hanson„ J. D. Cowan, C. L. Giles (San Marco, CA: Morgan Kaufmann), vol. 5, 1030-1037. Skaggs,W. E., McNaughton, B. L. (1996). Theta phase precession in hippocampal. Hippocampus, 6, 149-172. Softky, W.R., Koch, C. (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. Journal of Neuroscience, 13(1), 334-350. Somers, D., Nelson, S., Sur, M. (1995). An emergent model of orientation selectivity in cat visual cortical simple cells. Journal of Neuroscience 15, 5448-5465. Solstad, T., Boccara, C. N., Kropff, E., Moser, M. B., Moser, E. I. (2008). Representation of geometric borders in the entorhinal cortex. Science, 322(5909), 1865-1868. Strogatz, S. H. (2014). Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. Westview press. Stensola, H., Stensola, T., Solstad, T., Frøland, K., Moser, M. B., Moser, E. I. (2012). The entorhinal grid map is discretized. Nature, 492(7427), 72-78. Stepanyants, A., Hirsch, J.A., Martinez, L.M., Kisvárday, Z.F., Ferecskó, A.S., Chklovskii, D.B. (2008). Local potential connectivity in cat primary visual cortex. Cerebral Cortex, 18(1), 13-28. Sun, C., Kitamura, T., Yamamoto, J., Martin, J., Pignatelli, M., Kitch, L. J., ... Tonegawa, S. (2015). Distinct speed dependence of entorhinal island and ocean cells, including respective grid cells. Proceedings of the National Academy of Sciences, 112(30), 9466-9471. Tiesinga, P.H.E. (2002). Precision and reliability of periodically and quasiperiodically driven integrate-and-fire neurons. Physical Review E 65: 041913. Tiesinga, P., Fellous, J. M., Sejnowski, T. J. (2008). Regulation of spike timing in visual cortical circuits. Nature Reviews Neuroscience, 9(2), 97-107. Traub, R. D., Wong, R. K., Miles, R., Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of Neurophysiology, 66(2), 635-650. Tuckwell, H.C., Jost, J. (2010). Weak Noise in Neurons May Powerfully Inhibit the Generation of Repetitive Spiking but Not Its Propagation. PLoS Computational Biology 6(5): e1000794. doi:10.1371/journal.pcbi.1000794 Tuckwell, H.C., Jost, J., Gutkin, B.S. (2009). Inhibition and modulation of rhythmic neuronal spiking by noise. Physical Review E 80, 031907. Turrigiano, G.G. (1999). Homeostatic plasticity in neuronal networks: The more things change, the more they stay the same. Trends in Neurosciences, 22(5), 221-227. Turrigiano, G.G., Nelson, S.B. (2000) Hebb and homeostasis in neuronal plasticity. Current Opinion Neurobiology 10(3), 358-364. Turrigiano, G.G., Nelson, S.B. (2004). Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience 5(2), 97-107. Van Hooser, S.D., Heimel, J.A.F., Chung, S., Nelson, S.B., Toth, L.J. (2005). Orientation selectivity without orientation maps in visual cortex of a highly visual mammal. Journal of Neuroscience 25(1), 19-28. van Rossum, M.C., Bi, G.Q., Turrigiano, G.G. (2000). Stable Hebbian learning from spike timingdependent plasticity. Journal of Neuroscience, 20(23), 8812-8821. van Vreeswijk, C., Sompolinsky, H. (1996). Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science, 274(5293), 1724. van Vreeswijk, C., Sompolinsky, H. (1998). Chaotic balanced state in a model of cortical circuits. Neural Computation 10, 1321-1372. van Vreeswijk, C., Sompolinsky, H. (2005). Irregular activity in large networks of neurons In Chow C, Gutkin B, Hansel D, Meunier C, Dalibard J, editors, Les Houches Lectures LXXX on Methods and models in neurophysics, pp. 341-402, London. Elsevier. Wiesel, T.N., Hubel, D.H. (1974). Ordered arrangement of orientation columns in monkeys lacking visual experience. Journal of Comparative Neurology, 158(3), 307-318. Yartsev, M. M., Witter, M. P., Ulanovsky, N. (2011). Grid cells without theta oscillations in the entorhinal cortex of bats. Nature, 479(7371), 103-107. Zhang, S. J., Ye, J., Couey, J. J., Witter, M., Moser, E. I., Moser, M. B. (2014). Functional connectivity of the entorhinal–hippocampal space circuit. Phil. Trans. R. Soc. B, 369(1635), 20120516.
Subjects:Medicine > Neurosciences
Divisions:Gcia. de área de Investigación y aplicaciones no nucleares > Gcia. de Física > Sistemas complejos y altas energías > Física estadística interdisciplinaria
ID Code:1202
Deposited By:Tamara Cárcamo
Deposited On:09 Aug 2023 11:32
Last Modified:09 Aug 2023 11:32

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